The present invention relates to ultrasonic imaging and in particular to an apparatus and method for making ultrasonic strain measurements providing both axial, lateral and elevational strain for imaging with a sector or phased-array geometry with 1-D or 2-D transducers.
Elastography is an imaging modality that reveals the stiffness properties of tissue, for example, axial displacement, lateral displacement, elevational displacement, axial strain, lateral strain, elevational strain, Poisson's ratio, Young's modulus and other common strain and strain-related measurements. Strain measurements over an area may be mapped to a grey or color scale to form a strain “image.”
In quasi-static elastography, two images of tissue to be measured (a “pre-compression” and “post-compression” image) are obtained, typically by an ultrasound device, with the tissue in two different states of compression, for example, no compression and a given positive or negative (tensile) compression. Tissue may be compressed by an external agency such as a probe or the like, or by muscular action or movement of organs near the tissue.
Strain is deduced from these two images by computing gradients of the relative local shifts or displacement in the images along the compression axis. Quasi-static elastography is analogous to a physician's palpation of tissue in which the physician identifies firm structures by pressing the tissue and detecting the amount the tissue yields under this pressure.
Determining the relative displacement of tissue between the pre- and post-compression images may be done by analyzing portions of the ultrasonic images along a series of one-dimensional kernels normally extending along the axis of compression. The signal in each kernel in the pre-compression image is cross-correlated to the signal in a search area of the post-compression image. This cross-correlation process is repeated for many kernels in the pre-compression image yielding local displacement of tissue for each kernel. The gradient of these local displacements yields a measure of the local strains in the tissue.
The use of smaller kernels in this cross-correlation process can provide for improved image resolution. Yet if there are large displacements in the tissue, small kernels are subject to statistical miscorrelations leading to erroneous displacement results. In contrast, larger kernels provide more robust correlations over large search areas but can decrease the resolution of the image and can greatly increase the computational time.
U.S. patent application Ser. No. 11/384,607, filed Mar. 20, 2006, assigned to the same assignee as the present invention and hereby incorporated by reference, discloses a technique capturing the benefits of both small and large kernels through a multistep process where a larger kernel (typically with reduced sampling resolution) is used to obtain a coarse displacement map which is then used to guide the placement of small kernels to obtain a fine displacement map of high accuracy.
In two-dimensional elastography, strain is determined both in an axial direction aligned with the direction of compression and a lateral direction perpendicular to the direction of compression. The kernels in this case are two-dimensional and thus encompass greater amounts of data and must be cross-correlated in two dimensions. These factors significantly increase the computational burden of two-dimensional elastography.
In three-dimensional elastography, strain is determined both in an axial direction aligned with the direction of compression and lateral and elevational directions perpendicular to the direction of compression. The kernels in this case are three-dimensional and thus encompass greater amounts of data and must be cross-correlated in three dimensions. These factors significantly increase the computational burden of three-dimensional elastography.
Further complicating two and three-dimensional elastography, in many ultrasonic scanning systems the ultrasonic beam spreads outward in a fan beam. This requires that the kernels be sector shaped with the sector dimensions changing depending on the location in the imaged object and the amount of compression. The sector shape increases the complexity of two and three -dimensional cross-correlation over a larger area kernel.